Time2DigiWork
Researchers Involved
research areas
(job) crafting
culture
digital platforms
employee health and wellbeing
future of work
meaning
timeframe
2026 - 2026
contact
ameyer@ifi.uzh.ch
Time2DigiWork
While digitalization and AI have become central topics in higher education, empirical evidence remains scarce on how academic staff actually allocate their working time across different tasks and how digital tools and AI systems influence their efficiency and workload.
Building on the Digital Device Use Self-Monitoring Platform (D2USP) and the PersonalAnalytics toolkit developed by the UZH HASEL Lab, this study combines objective device-use data with self-reported information to gain a more comprehensive understanding of digital work patterns in academia. The D2USP is a customizable, privacy-considerate platform for collecting data on digital device usage across laptops and desktop computers and will also be further developed by the use in this project.
Background & Aim
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Quantify how university faculty distribute their working time across key tasks (teaching preparation, lecturing, grading, administration, research, meetings, leading projects, leading teams, etc.).
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Assess how digital tools and AI systems influence the efficiency, workload, and subjective time effectiveness of faculty work.
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Explore how academic leaders engage with AI and digital tools, identifying preliminary patterns in their use for time management and leadership tasks.
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Compare institutional contexts: Compare the use of digital tools and AI between a research-focused university (UZH) and an applied sciences institution (ZHAW).
Method
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Device logging (D2USP): Continuous, privacy-conscious collection of metadata (applications, active/idle times, duration, and switching patterns) from laptops and desktop computers. No content or keystrokes are captured.
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Experience Sampling (short self-reports): Participants complete short daily or weekly reflections on their current activities, perceived efficiency, and use of AI tools (e.g., ChatGPT, Claude, Notion AI, Copilot). The self-reports also capture specific purposes of AI use, such as for research tasks, grading or evaluating student work, and preparing lectures or teaching materials, leadership-related activities such as coordinating teams, and supervising projects.
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Baseline and follow-up surveys: Capture demographics, digital competence, academic workload, leadership responsibilities and AI attitudes.
This study will implement a modular design, developing separate modules to collect data on Faculty and staff members of ZHAW and UZH. Participants will be recruited through direct approach and internal communication in both universities.
Method
This study aims to generate initial empirical insights into how university faculty use digital tools in their daily academic work and to test the feasibility of combining device logging with short self-reports. Specifically, the study will deliver:
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A validated setup of the Digital Device Use Self-Monitoring Platform (D2USP) for academic contexts, including insights into data quality, user acceptance, and privacy handling.
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Descriptive insights into digital work time allocation (e.g., teaching preparation, research, administration, project management, leading) derived from objective device-use data at ZHAW.
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Empirical indications of AI-tool usage patterns and perceived efficiency among faculty, including typical use cases (e.g., preparing teaching materials, assessing student work, writing academic papers) at UZH and ZHAW.
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Objective real-world data as foundation for university policy in AI implementation.
The study will thus provide methodological validation, initial descriptive findings, and a refined study design for a subsequent, full-scale project.